In groups of patients with a recent myocardial infarction, several physiological measures of the heartbeat are known to be reasonably good prospective predictors of lethal arrhythmogenesis. Kleiger et al., (1987) showed that the reduced standard deviation of the R-R intervals was a statistically significant predictor. La Rovere et al., (1988) demonstrated that a reduced sensitivity of the R-R interval to high levels of blood pressure (i.e., a reduced baroreflex sensitivity) also was a predictor. It is interesting, however, that although the standard deviation of the R-R intervals and the baroreflex sensitivity are predictors between groups of subjects, when each measure is obtained from the same patient they are not found to be correlated (Bigger et al., 1989). This result could be explained by each being correlated with a third event.
The modulation of the heartbeat is attributed to three neural reflexes which occur during either respiration, low-level blood-pressure regulation, or body temperature change (Kitney, 1987). It has recently been proposed that in the normal heart this net modulation produces a "fractal" series of R-R intervals manifesting nonlinear dynamics that converts to a more regular and linear pattern when the heart becomes vulnerable to lethal arrhythmogenesis (Goldberger et al., 1988). Analysis of the heartbeat intervals with an algorithm for calculating the correlation dimension would be relevant to this hypothesis, because if the correlation dimension was found to be a fraction, it would imply a "fractal" time series. The "pointwise" correlation dimension (Mayer-Kress et al., 1988) has been used to evaluate R-R interval data, but, because of the large error of estimation, it has been impossible to know whether or not the calculated value is a fraction or an integer.
Using the Point-D.sub.2 algorithm set forth in this disclosure, to eliminate nonconvergent data points, Skinner, Carpeggiani, Landisman and Fulton (Circulation Research, 1990, submitted) analyzed records from eight conscious pigs before and during myocardial ischemia. They found that coronary artery constriction evokes a reduction in the correlation dimension that drops from 2.50 +/-0.81 during control periods to 1.07+/-0.18 during the minute just before the occurrence of the lethal arrhythmia, ventricular fibrillation.
The same effect has been observed by the researchers in a human patient who died while wearing a heartbeat monitor. That is, many minutes (and hopefully days) before the patient's heart manifested lethal arrhythmia, his heartbeat dimensionality showed excursions to the vicinity of 1.0. During the 13 minutes preceding the lethal arrhythmogenesis, the mean Point-D.sub.2 dropped to 1.0 and the standard deviation became markedly reduced.
It is simplistic to measure a specific point (usually R-R) of the heartbeat for a series of N heartbeats (wherein N typically is in the range of perhaps 5000 to 50,000), note the scatter, apply known statistical methods of analysis and thereby measure some precursor aspect of the heartbeat. As will be discussed with regard to one of the attached figures, a healthy heartbeat is shown with a particular heart rate (BPM or beats per minute) with a standard deviation; it is also presented for a heart in a failure mode where both the average heart rate and standard deviation are the same. Variations in heart rate do not express a failure condition solely through statistical analysis.
The present method and apparatus contemplate location of the peak in the heart rate signal obtained by appropriately attached electrodes which provide an output of the heartbeat signal. In the preferred version of the present apparatus, the signal is sampled and digitized to thereby convert from analog to digital values. The sampling rate need not be excessive; the sampling rate can be as low as perhaps 128 samples per second, the preferred rate being about 256 or 512 samples per second. While a greater number of samples can be obtained, there is an increase in precision as the sampling rate increases. The sampling rate can be carried to a desired level of precision. In any event, sampling and digitizing is carried out typically at that rate. For purposes of digitizing, an analog to digital converter (ADC hereinafter) makes measurements by means of a 12 or 14 bit digitizer. This level of accuracy or precision can be varied to obtain the precision desired. A sign bit is normally included with this data.
From beat to beat, the same part of the signal is located. This is preferably the peak which is normally termed the R complex, and as noted before, location of such peaks in a series of heartbeats in effect measures the time interval from one beat to the next or the R-R interval. Other parts of the signal waveform can be detected, but it is generally easier to locate the R complex. As a generalization, the heartbeat length evidences a certain degree of randomness which is indicative of a healthy heart, or one that is not subject to lethal cardiac arrest. More particularly, there is a measure of randomness that can be described as a specified range which is indicative of a healthy heart while in contrast a prearrest condition does exist where there is a conspicuous absence of randomness. As a matter of background, the heart is a highly innervated structure and requires the integrity of its nerves for coronary artery occlusion to evoke ventricular fibrillation (Ebert et al., 1970). A descending neural pathway from the frontal cortex to the brainstem cardiovascular centers must also be intact for coronary artery occlusion to result in a lethal arrhythmogenesis (Skinner and Reed, 1981). Clearly then, the nervous system has an important role in lethal arrhythmogenesis, and its randomness in operation is indicative of a healthy/lethal condition.
Several cerebral mechanisms with cholinergic projections to the heart are responsible for controlling the heartbeat intervals (Kitney, 1987). Increased mental task-load in humans will block this cholinergic regulation (Mulder and Mulder, 1987). Such mental stress (e.g., mental arithmetic) will also reduce the threshold for electrophysiological induction of ventricular fibrillation in patients with a myocardial infarction (Tavazzi et al., 1986). In animals mild stresses (e.g., an unfamiliar environment or mild cutaneous stimulation) must be present for coronary artery occlusion to result in ventricular fibrillation (Skinner et al., 1975).
The frontal lobe is electrically reactive to stressors. Tonically evoked activity in this structure appears to provide the output over the frontocortical-brainstem pathway that must be present for the initiation of ventricular fibrillation in the acutely ischemic heart (Skinner and Reed, 1981; Skinner, 1985). This output appears to inhibit modulation of the heartbeat (Skinner, 1985). In patients with a myocardial infarction, a reduced standard deviation of the R-R intervals predicts prospectively an increased incidence of mortality (Kleiger et al., 1987). A reduced baroreflex sensitivity to transiently induced high blood-pressure is also predictive of increased mortality in patients with a recent infarction (Schwartz et al., 1984). Thus the study of the heartbeat dynamics, which are controlled by specific cerebral mechanisms, may provide insight into the mechanism by which ventricular fibrillation is generated.
The observation of mathematical chaos in the heart may provide innovative ways for the cardiologist to monitor the sick, injured or aging heart and then provide the appropriate therapy. Under healthy conditions, the heartbeat is surprisingly erratic with spectra and phase space representations consistent with mathematical chaotic dynamics. Patients at increased risk of sudden cardiac death may show a loss of this physiological chaos, as well as abrupt changes resembling the bifurcations observed in the "logistic" function. Detection and quantification of such nonlinear dynamics in the heartbeat timing may provide an earlier early warning system for cardiac diseases and drug toxicities, as well as a new way to monitor the effects of aging (Furman et al., in press Goldberger et al., 1988, 1990, in press; Stambler et al., 1989).
Currently the analysis of electrocardiographic data from cardiac monitors is quite superficial, with attention paid primarily to the mean and standard deviation of heart rate, along with counts of various types of abnormal heartbeats. Yet, as illustrated in FIG. 1, two subjects may have virtually identical means and standard deviations of heart rate, but the patterns (i.e., dynamics) are different and so are the vulnerabilities to lethal arrhythmogenesis. It is the dynamics that carries the important diagnostic and prognostic messages enabling medical intervention.
Until recently, it was widely held that sudden cardiac death represented an abrupt change from the apparently periodic state of the normal heartbeat to one in which chaotic arrhythmias occur. Mathematically speaking, the word "chaos" means that when two points are placed next to each other but on different orbits of their attractor, they will get farther and farther away from each other as they travel in time over their separate trajectories through phase space (i.e., they will have divergent trajectories and therefore have at least one positive Lyapunov exponent). Work from Goldberger and associates, as well as others, has suggested that under normal conditions the heart has chaotic dynamics and that fatal disturbances of the cardiac rhythm are often preceded by a decrease in the degree of physiological chaos (Furman et al., in press; Goldberger et al., 1988, 1990, in press; Stambler et al., 1989). This represents a reversal in the usage of the term "chaos" when applied to the injured heart.
This reversal in perspective above chaos in the heart is illustrated in FIG. 2. It is the healthy heart that has a chaotic pattern in phase space. Goldberger and associates have reported two abnormal heart rate patterns, as shown in FIG. 3, in patients with severe left ventricular failure (a group at high risk of sudden death) and in patients who actually sustained a fatal or near fatal tachyarrhythmia while wearing a portable electrocardiographic monitor (Goldberger et al., 1988; 1990). One dynamic was termed the oscillatory pattern because it is characterized by low frequency (0.01-0.06 Hz) oscillations in sinus heart rate. The other dynamic they called the flat pattern because it is characterized by a marked reduction is beat-to-beat variability. These pathologic patterns are reminiscent of the reduction in heart rate variability and the low frequency oscillations ("sinusoidal" pattern) observed in the fetal distress syndrome. Similar dynamics occur prior to cocaine induced sudden death in ferrets (Stambler et al., 1989). A reduction in heart rate variability and its chaotic dynamics also occurs in aging (Furman et al., in press).
Based on finding such as these, cardiologists are now pursuing ways of identifying nonlinear transitions such as bifurcation behavior and the loss of physiological chaos that may precede fatal cardiac disturbances. A variety of approaches are being followed, including spectral estimates, measurement of Lyapunov exponents and calculation of the correlation dimension. Important mathematical and technical issues, such as the problem of biological stationarity and the accuracy of computerized algorithms, need to be resolved before such measurements can be reliably applied to biological data sets and computed "on-line" by physiological monitors. The on-line monitoring process will be described to include the initial step of setting up the process by loading a set of dynamic data. Once loaded, and dependent on CPU speed, calculations relating to a single heartbeat can be run as soon as that beat has been completed, and the data provided is almost instantaneous. Those in the field hope that within the coming decade assessment of such nonlinear indices will provide cardiologists with important new techniques for identifying high risk patients at an earlier stage in the development of their disorder. These new indices may also enable more appropriate observation of the pathology, as they are linked more directly to the mechanism of arrhythmogenesis and not merely attached to a correlate, such as heart rate variability, that may or may not be appropriate. Such innovations are expected to allow detection prior to the manifestation of the life threatening and fatal arrhythmias and to be more effective in monitoring clinical treatments. An example of one device is described in the brochure from Cherne Medical. This device and method claims 75% correspondence between the chaotic analysis of one heartbeat recorded simultaneously from many electrodes and the degree of coronary artery occlusion as determined by coronary angiography in a cardiac catheter laboratory in a medical center hospital. That technology is clearly divergent from that to be described below in which the Point-D.sub.2 method is used to evaluate vulnerability to lethal arrhythmias.
Ideker and colleagues (Chen P-S et al., 1986) are taking advantage of recent advances in electronic and computer technology to investigate the mechanisms by which the lethal cardiac arrhythmia is initiated. They record simultaneously from 138 or more electrodes placed on and in the hearts of animals and of patients undergoing heart surgery to track the spread of the wavefronts of cardiac excitation during fibrillation (Ideker et al., 1987). They also developed techniques to record potentials created throughout the heart by large electrical stimuli, such as defibrillation shocks. These investigators are using such instrumentation to investigate the mechanisms by which large electrical stimuli delivered during the recovery portion of the cardiac cycle, called the vulnerable period, can initiate ventricular fibrillation.
Before the advent of computer-assisted cardiac mapping techniques, the principal cause for the electrical initiation of fibrillation during the vulnerable period was thought to be heterogeneities in the electrophysiological characteristics of the myocardial cells (Han and Moe, 1964). As stated by the non-uniform dispersion of refractoriness hypothesis, a large electrical stimulus given during the vulnerable period will conduct away slowly from the site of stimulation because the cells are not yet fully recovered. If the refractory periods of the individual cells are dispersed nonuniformly, then the activation arising at the site of electrical stimulation will fail to propagate into regions that are still refractory, while it will propagate into neighboring regions that are more recovered. If a more refractory region has sufficiently recovered during the time the activation front propagates through any adjacent less refractory region, then this activation front can enter the distal portion of the more refractory region and propagate back towards the site of electrical stimulation. If the tissue first excited by the electrical stimulus has had time to recover, it can then be re-excited by the propagating activation front, producing a so-called reentrant circuit. If this reentrant circuit persists and begets daughter reentrant circuit, then ventricular fibrillation is thought to be produced.
The present invention applies an analysis to the R-R interval measurements. Thus, these measurements are analyzed to locate a subtle representation of the randomness which is present in the data. This has several benefits. It is able to arrive at a useful indicator with a smaller set of data. By contrast, perhaps 5000- 50,000 or more heartbeats were required heretofore, but the present approach uses as few as only 1000 heartbeats, and it is thought to be possible to make this analysis wherein N is in the range of about 2500 or less, the lower limit not yet being determined. Moreover, it is able to accomplish that analysis in relatively quick order. It can, in fact, provide an indication for an individual as opposed to merely grouping the individual in broad categories (i.e., healthy versus unhealthy). The output of the present procedure is a specific indication of vulnerability to lethal myocardial infarction (MI hereinafter). That indication can be expressed as a simple number with an indication of the statistical standard deviation (SD hereinafter) attached. For instance, a healthy heart is indicated by a measure of 2.10 while an unhealthy heart is indicated by an evaluation of 1.0. In very general terms, chaos analysis, as taught herein, does not require stationarity of the biological generator (i.e., the human nervous system operating the heart in timed fashion) and relies on the lack of stationarities and makes measurements which are then used in an algorithm for determining the correlation dimension (D.sub.2 hereinafter). This involves the sequential interaction of a routine for determining the scaling region over which the point scaling dimension is evaluated and another routine which determines whether or not the slope of the scaling region converges to a horizontal asymptote as the embedding dimension increases in value. The height of this asymptote is D.sub.2. One of the benefits of this is the total number of data is so much smaller. Another advantage of the present apparatus is that the data which is obtained by the system can be analyzed almost in real time. It can operate readily with a patient whose heart rate is 60-90 BPM and thereby provide analysis of this data at rates ranging from 50% to 100% of the heart rate depending on the speed of the computer used. Utilizing a personal computer based on the 386 Intel brand processor installed into a machine with adequate memory and capable of operations at perhaps 16 MHz up to perhaps 30 MHz, substantially real time operations can be achieved. Moreover, this can be accomplished without an excess memory capacity. This is accomplished with a conventional set of electrodes installed in a conventional pattern for measuring the heart beat. Many other advantages of the present apparatus will become apparent on a review of the below included specification in conjunction with the drawings which are attached.